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Here's what most people miss about AI in recruiting: The problem isn't finding candidates. It's knowing which ones are still available when you're ready to hire. A leading financial services firm had 70-80 qualified candidates sitting in their ATS after a hiring freeze. When hiring resumed, their recruiters faced weeks of manual searches to verify employment status. Top talent went cold while they checked "open to work" signals one profile at a time. This is the invisible tax of manual verification—it delays your best hires and burns recruiter time on data work, not conversations. We built them an AgenticAI system that does the heavy lifting in minutes, not weeks. It ingests resumes directly from their ATS, matches employment data with 90% confidence, and scores candidates on availability and fit. Their recruiters get a real-time dashboard showing exactly who's ready for outreach. The result? They re-engaged 70+ qualified candidates, cut verification time by 99%, and freed their team to do what they're actually good at: building relationships. We deployed the MVP in 10 days. Total project timeline? Four weeks—not the 3-4 months a traditional build would take. This is what production-ready AI looks like when you're not starting from scratch. Read the full story → https://lnkd.in/g7P-2ARR

The largest privately held logistics firm in the US had a customer service team drowning in 155 daily emails. 83% were identical shipping questions—tracking updates, package status, and missing packages. Their CSRs spent entire days triaging repetitive requests while complex issues requiring human judgment piled up. Standard problem: high volume, manual responses, no way to scale without adding headcount. We deployed a working AI chatbot in 60 days. Trained on their actual email patterns, it now handles 60%+ of tickets instantly—tracking requests, status updates, and routine information. Their CSRs focus on complex cases. The system runs 24/7 across 200+ countries. Production-grade, scalable, and it actually shipped. No six-month roadmap. No endless discovery phase. Just working on automation. Read the full story → https://lnkd.in/gbKDn42m

Here's what three decades of enterprise engineering taught us: the gap between AI demos and production systems is where most projects die. We built Tenjumps to close that gap—deploying working automation from 300+ battle-tested components while others are still writing proposals. Mid-market enterprises and scale-ups get Fortune 500 capabilities without the Fortune 500 timeline. Curious about what makes us different? Let's talk.

Sarah's Breaking Point Sarah runs operations for a mid-sized logistics company. Last Tuesday, she watched her customer service manager stay until 9 PM answering the same shipping questions. Again. 155 emails a day. 83% identical. "Where's my package?" "Check the tracking?" "What's the status?" Sarah knew AI could help. She'd sat through the demos. Then came the six-month roadmaps, the "discovery phases," the budgets she didn't have. Then she called us. We asked one question: "What's drowning your team right now?" She told us about the emails. We told her we'd built this before – customer service automation already working in production elsewhere. "How long?" she asked. "10 days to get it running." But we weren't starting from scratch. We had the components – email parsing, ticket classification, response generation. We'd already debugged the complex parts. We just needed to teach it her business. Ten days later, it was live. 60 days later? 60% of tickets are automatically resolved. Her manager left at 5:30 PM. Sarah told us: "I didn't need an AI strategy. I needed someone who'd actually built this before. You just asked what was broken and fixed it." What's your version of those 155 emails? Candidate verification? Compliance reporting? Something we haven't seen yet? Let's talk → https://tenjumps.com/about

Your business wants AI yesterday. Vendors keep overpromising. Another demo that looked great until integration, another pilot that can't handle production load, and another consultant who's never actually shipped code or AI. Meanwhile, your backlog grows and everyone's asking why AI isn't "just working." We get it. We're engineers too. We don't pitch vaporware. We deploy from 300+ production-tested components. Real infrastructure. Real monitoring. Real SLAs. What this looks like: Customer service bot: 10 days to production (not a toy demo) HR verification system: 4 weeks, 90% confidence, integrated with existing ATS Your stack. Your security requirements. Your compliance. We know what breaks in production. Built the fallbacks. Handled the edge cases. One call. We'll talk architecture, not buzzwords. Show us your environment. We'll show you what we can deploy that actually scales. Let's build something that works → https://lnkd.in/g-7Xy46h

Responsible data engineering matters when you're managing legacy systems, multiple business units, or data brought together through an acquisition. Four practices can help: 1. Modernize deliberately Understand dependencies and business requirements before deciding what to replace, consolidate, or retire. 2. Build ownership into the SDLC Clear ownership, monitoring, testing, and lineage make data easier to maintain and govern. 3. Match freshness to the decision Not every workload needs real-time processing. The right service level depends on what the data supports. 4. Measure what changed A system moving to the cloud doesn't automatically mean modernization succeeded. Look at reliability, cost, performance, data quality, and business impact. Modernization is an opportunity to decide what should move forward and not simply move everything into a new environment. Learn more on our website: https://lnkd.in/eiKYgCEb #DataEngineering #DataStrategy #LegacyModernization